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Proceedings Paper

Image feature analysis for classification of microcalcifications in digital mammography: neural networks and genetic algorithms
Author(s): Chris Yuzheng Wu; Osamu Tsujii; Matthew T. Freedman; Seong Ki Mun
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Paper Abstract

We have developed an image feature-based algorithm to classify microcalcifications associated with benign and malignant processes in digital mammograms for the diagnosis of breast cancer. The feature-based algorithm is an alternative approach to image based method for classification of microcalcifications in digital mammograms. Microcalcifications can be characterized by a number of quantitative variables describing the underling key features of a suspicious region such as the size, shape, and number of microcalcifications in a cluster. These features are calculated by an automated extraction scheme for each of the selected regions. The features are then used as input to a backpropagation neural network to make a decision regarding the probability of malignancy of a selected region. The initial selection of image features set is a rough estimation that may include redundant and non-discriminant features. A genetic algorithm is employed to select an optimal image feature set from the initial feature set and select an optimized structure of the neural network for the optimal input features. The performance of neural network is compared with that of radiologists in classifying the clusters of microcalcifications. Two set of mammogram cases are used in this study. The first set is from the digital mammography database from the Mammographic Image Analysis Society (MIAS). The second set is from cases collected at Georgetown University Medical Center (GUMC). The diagnostic truth of the cases have been verified by biopsy. The performance of the neural network system is evaluated by ROC analysis. The system of neural network and genetic algorithms improves performance of our previous TRBF neural network. The neural network system was able to classify benign and malignant microcalcifications at a level favorably compared to experienced radiologists. The use of the neural network system can be used to help radiologists reducing the number biopsies in clinical applications. Genetic algorithms are an effective tool to select optimal input features and structure of a backpropagation neural network. The neural network, combined with genetic algorithms, is able to effectively classify benign and malignant microcalcifications. The results of the neural network system can be used to help reducing the number of benign biopsies.

Paper Details

Date Published: 25 April 1997
PDF: 9 pages
Proc. SPIE 3034, Medical Imaging 1997: Image Processing, (25 April 1997); doi: 10.1117/12.274136
Show Author Affiliations
Chris Yuzheng Wu, Georgetown Univ. Medical Ctr. (United States)
Osamu Tsujii, Georgetown Univ. Medical Ctr. (United States)
Matthew T. Freedman, Georgetown Univ. Medical Ctr. (United States)
Seong Ki Mun, Georgetown Univ. Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 3034:
Medical Imaging 1997: Image Processing
Kenneth M. Hanson, Editor(s)

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